Tell us about your role
– I have taken on the role of Predictive Maintenance Lead in the Asset Analytics team within the Data Services Business Unit. My main responsibilities include overseeing the development and delivery of the Predictive Maintenance project stream within the Asset Analytics team and collaborating with clients on Data Science projects to drive value. I serve as a subject matter expert in the area of rolling stock and predictive maintenance and work closely with the Product Owner and DS team to grow the business. My main focus is to conceptualize, design, test, and implement new Predictive Maintenance/Prognostic Health Management/Intelligent Asset Management models for various components of rolling stock in close collaboration with the team.
What is the best part of working within this industry?
– One of the greatest advantages of being in this industry is the opportunity to assist our clients in minimizing downtime through the creation of insightful solutions aimed at enhancing their asset management. These solutions not only help to increase efficiency and productivity, but also provide our clients with peace of mind knowing that their assets are being properly monitored and managed. This, in turn, allows them to focus on other aspects of their business without having to worry about the state of their assets. By utilizing the latest technologies and innovative approaches, we strive to continuously improve and evolve our solutions to better meet the ever-changing needs of our clients.
Tell us about your background/competence within the industry
– I am currently in the final stages of completing my PhD in the Rail Vehicles Research Group at Engineering Mechanics at KTH, Stockholm. My primary area of expertise is in the interaction between vehicles and tracks, specifically the running dynamics of rail vehicles. Over the past four years, I have been focusing my PhD research on the onboard monitoring of vehicle-track interaction through the use of machine learning. I have a strong background in mechanical and vehicle engineering, and during my PhD studies, I have developed advanced skills in data science and machine learning. During my PhD research I have worked on various industry-funded projects with companies such as SJ and TrV. More specifically, I have worked with developing advanced machine learning algorithms to analyze X2000 fleet’s onboard data and assist SJ technicians for better fault identification and effect maintenance.
Did you know about Atkins before you started?
– Yes, since I started my education at KTH, I have heard of Interfleet/SNC and now Atkins. I guess, it has been kind of a tradition to join interfleet/ SNC after finishing PhD from Rail Vehicles research group at KTH.
